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9.1 Operational AI Applications: Computer Vision Quality Inspection and Sensor-Driven Predictive Maintenance

I. Quality and Maintenance as Decision Problems

II. Computer Vision for Visual Inspection

III. Setting the Threshold: The Hitachi Omika Works Case

IV. Sensor-Driven Predictive Maintenance

V. Edge, Cloud, and Latency

Section Summary

9.2 Financial Anomaly Detection and Alert Fatigue: False Positives, Investigator Capacity, and Threshold Calibration

I. The Alert-Fatigue Problem

II. Why Monitoring Systems Produce So Many False Positives

III. Pricing the Errors: Precision, Recall, and Weighted Loss

IV. Capacity and Risk-Based Calibration

V. Alert Management and Supervisory Documentation

9.3 Generative and Agentic AI in the Finance Back Office: Automation, Data Foundations, and Controls

I. From Copilots to Agents

II. Where Back-Office AI Is Most Mature

III. Why Finance AI Stalls: The 10/20/70 Rule

IV. Semantic Harmonization and Driver-Based Analysis

V. Governance as Architecture

9.4 Threshold Calibration in Practice: Cost Curves, Operating Posture, Score Distributions, and Statistical Baselines

I. Why Unweighted Measures Mislead

II. The Cost Matrix and the Cost Curve

III. Operating Posture: Review or Block

IV. Reading Model Curves and Score Distributions

V. Statistical Baselines and Recalibration

9.5 US Legal Constraints on Model-Driven Decisions: Colorado's ADMT Law, Meaningful Human Review, FDA Oversight, and Litigation Risk

I. The US Legal Map for Model-Driven Decisions

II. Colorado's SB 26-189: Automated Decisions in Consequential Domains

III. Meaningful Human Review

IV. FDA Oversight of AI-Enabled Medical Devices

V. Litigation and Contract Risk

9.6 Evidence, Explanation, and Auditability in Regulated Decisions: Model Risk Management, Documentation, and Adverse-Action Reasons

I. The Evidence a Regulated Decision Needs

II. Model Risk Management

III. Documentation: Model Cards, Lineage, and Decision Records

IV. Two Audiences: Explaining to a Regulator and to a Customer

V. International Contrast: The EU AI Act

Knowledge Check

Knowledge Check 1: Quality and Maintenance as Decision Problems

Knowledge Check 2: Computer Vision for Visual Inspection

Knowledge Check 3: Setting an Inspection Threshold

Knowledge Check 4: Sensor-Driven Predictive Maintenance

Knowledge Check 5: Edge, Cloud, and Latency Tradeoffs

Knowledge Check 6: The Alert-Fatigue Problem

Knowledge Check 7: Sources of False Positives

Knowledge Check 8: Precision, Recall, and Weighted Loss

Knowledge Check 9: Capacity and Risk-Based Calibration

Knowledge Check 10: Copilots and Agents in Finance

Knowledge Check 11: Finance AI Maturity and the 10/20/70 Rule

Knowledge Check 12: Semantic Harmonization and Governance Architecture

Knowledge Check 13: Cost Matrices and Cost Curves

Knowledge Check 14: Review-or-Block Operating Postures

Knowledge Check 15: Score Distributions, Baselines, and Recalibration

Knowledge Check 16: Colorado ADMT Requirements and Meaningful Human Review

Knowledge Check 17: FDA Oversight and Litigation Risk

Knowledge Check 18: Evidence, Documentation, and Auditability

Welcome to "AI Decision Systems in Operations, Finance, and Regulation," a comprehensive course designed to bridge the gap between artificial intelligence capabilities and real-world business applications. In today's rapidly evolving digital landscape, organizations are increasingly relying on AI to drive operational efficiency, manage financial risks, and navigate complex regulatory environments.

Welcome to "AI Decision Systems in Operations, Finance, and Regulation," a comprehensive course designed to bridge the gap between artificial intelligence capabilities and real-world business applications. In today's rapidly evolving digital landscape, organizations are increasingly relying on AI to drive operational efficiency, manage financial risks, and navigate complex regulatory environments. This course provides a deep dive into the practical implementation of AI decision systems across critical, high-stakes business functions.

You will explore operational AI applications, including computer vision for quality inspection and sensor-driven predictive maintenance, learning how to balance edge and cloud computing for optimal latency. The curriculum then transitions into the financial sector, tackling the pervasive issue of alert fatigue in anomaly detection. You will learn how to calibrate thresholds, manage false positives, and implement generative and agentic AI in the finance back office while adhering to strict governance frameworks.

Crucially, this course addresses the legal and regulatory constraints surrounding model-driven decisions. From understanding Colorado's ADMT Law and FDA oversight to mastering model risk management, documentation, and auditability, you will gain the expertise needed to ensure compliance and mitigate litigation risks. Whether you are optimizing cost curves or ensuring meaningful human review, this course equips you with the strategic frameworks and technical insights to deploy AI responsibly and effectively in highly regulated domains.

Chapter 9: AI Decision Systems in Operations, Finance, and Regulation

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            <p>Artificial Intelligence is fundamentally transforming how organizations make critical decisions across various sectors, moving beyond simple automation to complex, high-stakes decision systems. This course explores the intricate intersection of AI applications in operations, finance, and regulatory compliance. By examining real-world implementations—from computer vision in manufacturing quality inspection to sensor-driven predictive maintenance—learners will understand how AI models are deployed to optimize efficiency while navigating the physical constraints of edge and cloud computing. The curriculum dives deep into the mechanics of these systems, illustrating how operational challenges are framed as data-driven decision problems that require precise technological solutions.</p>

<p>Beyond the factory floor, the course tackles the sophisticated use of AI in the financial sector, addressing critical issues such as anomaly detection, alert fatigue, and the transition from AI copilots to autonomous agents in the back office. A central focus is placed on the rigorous calibration of these models, teaching participants how to balance precision and recall using cost curves, score distributions, and risk-based thresholds. Furthermore, as AI systems increasingly influence consequential decisions, understanding the legal and regulatory landscape is paramount. The course provides a comprehensive overview of US legal constraints, including Colorado's ADMT law and FDA oversight, alongside global frameworks like the EU AI Act. Learners will master the principles of model risk management, ensuring that AI-driven decisions are backed by robust evidence, meaningful human review, and transparent auditability.</p>
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